Multi-parameter patient monitoring in intensive care units (ICU) remains unsatisfactory as evidenced by the well-known alarm fatigue problem. It has been reported that a critical care patient could generate 700 alarms per day. At UCSF, a daily average of 187 audible alarms per bed occurred in ICUs during one month of assessment. Improving signal processing algorithms, fine-tuning alarm thresholds, and downgrading some alarms to an inaudible category are some ways to address alarm fatigue. However, these interventions attempt to solve the problem within the context of conventional patient monitoring practice (i.e., the focus is on an individual alarm while ignoring the relationships among alarms, the contextual information established by other data available in electronic health record (EHR), and the sequential patterns of all of these variables). In fact, we argue that alarm fatigue reflecs a deeper challenge for critical care clinicians who are overloaded with increasingly available raw data but do not have appropriate tools to leverage the potential of these data to treat their patients. As a first step to support clinicians to overcome data overload, our goal is to precisely detect gross patient state changes by recognizing combinatorial and sequential patterns among individual alarms, physiological variables, and EHR data. Achieving this goal will lead to developing additional decision support tools to understand the causes and select potential interventions for the detected patient state changes. Our group has done preliminary studies that demonstrate the feasibility of achieving this goal. In particular, we have evolved a specific algorithm to identify co-occurring monitor alarms, which frequently precede in-hospital cardiopulmonary arrests (CPA) but rarely occur among control patients, to a data fusion framework. This framework is capable of recognizing predictive combinations of a much richer set of variables including lab tests and additional physiological variables not available from monitors. We term these combinations SuperAlarm patterns. By construction, SuperAlarm triggers will occur much less frequently than monitor alarms, yet be more precise in detecting patient state changes. Thus, the objective of this application is to develop and validate further algorithm improvement under this SuperAlarm data fusion framework using prospective data. We will pursue the following three aims: 1) To enrich SuperAlarm patterns by novel analysis of Electrocardiographic (ECG) signals; 2) To develop sequential pattern recognition methods for sequences of SuperAlarm triggers. 3) To conduct prospective data collection to develop and validate SuperAlarm model.